Coefficient Of Determination Research Articles

Identifying the Relationship Between CGM Time in Range and Basal Insulin Adherence in People With Type 2 Diabetes.

The study aimed to determine the relationship between basal insulin adherence and glycemic control evaluated by time in range (TIR) in people with insulin-treated type 2 diabetes (T2D), using data from both continuous glucose monitors (CGM) and connected insulin pens. Furthermore, the study aimed to determine the best basal insulin adherence metric. CGM data and basal insulin data were collected from 106 insulin-treated people (aged ≥18 years) with T2D. Three different adherence metrics were employed (dose deviation, dose deviation ≤20%, and a traditional metric) and a three-step methodology was used to measure insulin adherence level. The coefficient of determination (R2), based on a univariate linear regression analysis, was used to determine the relationship between each adherence metric and TIR. A statistically significant relationship was observed between TIR and adherence quantified as the dose deviation ≤20% metric (R2 = 0.67, P = .006). Neither the relationship between the dose deviation metric and TIR (R2 = 0.43, P = .08) nor the relationship between the traditional metric and TIR (R2 = 0.35, P =.23) was found to be statistically significant. Our study indicates a relationship between basal insulin adherence and TIR in people with insulin-treated T2D. This seems to underscore the role of basal insulin adherence for optimal glycemic outcomes and utilizing TIR as a clinical marker. Furthermore, the results suggest that the magnitude of deviation from the recommended basal insulin dose impacts glycemic control, indicating dose deviation ≤20% as a more accurate metric for quantifying adherence.

Synergistic Effect of Influence Factors on Ammonia Volatilization in Fertilizer Solution: A Laboratory Experiment

Ammonia volatilization, which is one of the main ways that nitrogen gas is released from farmland, restricts promotion of the utilization of nitrogen fertilizer and contains some potential environmental risks. To investigate the general pattern of ammonia volatilization under actual paddy field conditions, we designed an indoor simulated system to measure the amount of ammonia volatilized within a single time period by controlling the pH and concentration of NH4+ (c(NH4+)) in the solution, the gas–liquid interfacial gas velocity, and the ambient temperature. In this paper, the influence of these factors, the synergistic effect on ammonia volatilization, and their quantitative relationship are discussed. We used solutions of ammonium bicarbonate (SAB) and diammonium phosphate (SDP) for the simulation experiments, and the results showed that there is a significant linear relationship between the amount of ammonia volatilization and c(NH4+). The correlation coefficients were between 0.9214 to 0.9897 and 0.8932 to 0.9904 for SAB and SDP, respectively. The quantitative relationship between temperature and pH and the influence factor (CIF) and the initial ammonia volatilization fluxes (IAVFs) was analyzed by the least-squares method, and the degrees of polynomial were one and two, respectively. The regression equations of the SAB and SDP among the amount of ammonia volatilization with the concentration of ammonium nitrogen, the temperature, and the pH were calculated by using MATLAB. Considering the effects of temperature and pH on the CIF and IAVFs under individual conditions, we used a binary cubic model to fit the relationship between temperature and pH to the CIF and IAVFs. The simulation results showed that the correlation coefficients between the CIF and IAVFs for SAB were 0.9980 and 0.9680, respectively, and the correlation coefficients were 0.9946 and 0.9708 for SDP, respectively. The quantitative equation took into account the coefficient of determination and degrees of polynomial, and the ammonia volatilization fluxes can be calculated by using these equations.

Vetting the Extrapolation Caution of Tamhane & Dunlop: A Time Series Forecasting Analysis

Context In forecasting there are only two classes of Projections: Extrapolations or Interpolations. Interestingly, Interpolations usually, all but guarantee, that the theoretical expectations that rationalize the veracity of the forecasting profiles are the likely State of Nature. Simply: The [1-a]% Forecasting Prediction Interval [FPI] provides the expected-intel with a frequency of [1-a]%. However, for Extrapolations, [T&D] [8 p. 363] offer the following “Forecasting Bump in the Road” T&D Conjecture: “- - -extrapolation beyond the range of the data is a risky business should be avoided.” If T&D are correct that: Forecasting Extrapolations somehow compromise the linkages of the Projection of the Past into a Relevant Future, Then, indeed, Extrapolations should be avoided. T&D’s theoretical and experientially based caution has garnered very little vetting interest—Voilà: The motivation for our research report. Research Elements Initially, we offer a detailed analysis of the mathematical nature of an Extrapolation as it affects the Precision of the 95% Time Series [TS] Forecasting Prediction Interval [95%TSFPIs]. This is critical as, interestingly, T&D offer no detailed support for their risky business musing. For the inferential testing stage, we will use as the Y-Response Variate: The Capture Rate of the 95%TSFPIs. As we are using a DOE-Inferential Model, we are opting for two-Category Covariates: Extrapolations—[As they are the focus of our inquiry.] & Panel-Variation—measured as the Coefficient of Determination [CoD]—[As variation is the bane of forecasting.]. For DOE: testing, we have selected a random sample of 202 Panels from the S&P500; for each Panel there are Three TS-Extrapolations and also Three [CoD-screens]. Results We found clear inferential evidence that as the Extrapolations move away from the mean of the TS time-index & The CoD increases that certain DOE-{Ext & CoD} Tukey-Kramer HSD-interaction-profiles suggest a failure to realizes the theoretical expectations of the Capture Rate of the 95%TSFPIs. We offer, for the first time, a deconstruction of these very interesting inferential DOE-results that rationalize T&D’s risky business musing.

Mathematical Modeling and Optimization of Enzyme‐Based Amperometric Screen‐Printed Biosensors Using Horseradish Peroxidase as a Model

AbstractIn this study, a mathematical model was formulated to characterize the functioning of third‐generation enzyme‐based amperometric biosensors. This study utilized a horseradish peroxidase (HRP)‐based, screen‐printed biosensor. The model parameters were determined using the Environment for Modeling Simulation and Optimization (EMSO), validated with experimental data, and exhibited high determination coefficients, indicating the accuracy of the models in capturing biosensor behavior. Simulations based on this model emphasize diffusion as a limiting factor. Further sensitivity analysis was conducted using simulated response surfaces and biosensor performance optimization, which confirmed that higher concentrations of HRP in the thinnest polymeric film resulted in enhanced biosensor sensitivity.

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction.

A Comparative Growth Trend Study of Tomato in Jaipur and Sirohi District of Rajasthan, India

Tomato (Licopersicon esculentum) is an important crop that contributes economically and significantly to horticulture production. T The study was conducted for the period of 12 year in Jaipur and Sirohi districts of Rajasthan to analyze the growth rate in area, production, and productivity from 2010-11 to 2021-22. (Department of Horticulture, Government of Rajasthan-2022-23). For analyzing the trend in area production and productivity of tomatoes in Jaipur and Sirohi district, and Rajasthan state the following linear, semi-log, and exponential functions were tested. The tested model were selected based on high R2 (Coefficient of determination) and the best-fit function was used for the result discussion. Semi-log function was best function for the area, production and productivity of Rajasthan. The compound annual growth rate (CAGR) for area of Rajasthan was found 1.76 percent, with 0.46 R2. The production and productivity show 9.91 and 8.15 percent growth rate. The semi-log function was also best fit the area and production of Tomato in Jaipur district with 4.58 and 15.94 percent annual growth respectively. The linear function was fitted for the productivity that shows 17.20 percent CAGR with 0.39 percent R2. The area of tomato in Sirohi district shows highest fluctuation due to this an exponential function was shows poor fits for calculate the growth rate this function shows 0.01 percent compound annual growth rate. The exponential function was best fit for the production and productivity with 0.41 and 0.31 R2 respectively. The exponential function show 11.24 percent growth rate, which was significant at 5 percent level. The productivity show 12.25 percent compound annual growth rate with 10 percent level of significance. Thus the productivity shows highest compound annual growth rate followed by Production and area in Jaipur and Sirohi district of Rajasthan, while in Rajasthan, production shows highest growth rate followed by productivity and area. According to the findings, the result suggest for the introduction of high yield varieties of tomato may increase the productivity in Rajasthan.

On the issue of impact of income inequality on the socio-economic development of territories

The issues of economic inequality and justice were considered in the works of the philosophers of Ancient Greece. Over time, the problem of income inequality has not lost its relevance, which is confirmed by domestic and foreign studies. Until 2015, the capital of the Russian Federation had the highest value of the Gini index, which indicates a high degree of inequality in income distribution in the territory of this subject. Over time, the situation has changed, in particular, by increasing the incomes of the least well-off groups of the population, providing them with subsidies and payments. The purpose of the study is to analyse to what extent these changes have an impact on the socio-economic development of the region. With the help of econometric calculations, the degree of dependence between the equity index and the value of the gross regional product per capita has been determined. It is revealed that there is an inverse relationship between the considered indicators, that is, a reduction in income inequality has an impact on the growth of regional production. The high value of the coefficient of determination indicates the high quality of the constructed model. The possibility of using the simulation results in practice is confirmed by calculations of the average approximation error and the Fisher criterion.

From dishwasher to river: how to adapt a low-cost turbidimeter for water quality monitoring.

This study presents the process of design and development of a low-cost turbidimeter for monitoring water quality, facilitating rigorous spatial-temporal variability analysis within large-scale hydrological systems. We propose a low-cost optical turbidimeter, modifying the existent SEN0189 turbidity sensor, Arduino boards, and additional sensors for temperature compensation. We compared a low-cost system with high-tech sensors, modifying the original low-cost SEN0189 probe for enhanced environmental performance. The three-step methodological framework involved prototype development, compensation for environmental factors, and preparation for future field deployment. Calibration equations with a high coefficient of determination and a temperature correction equation were established. We made adaptations to overcome field deployment challenges, including a 3-D printed sensor case, defining the relationship between measurement uncertainty and energy consumption, and specifying field installation guidelines. In summary, this study presents a comprehensive approach to a low-cost optical turbidity system, demonstrating its potential for accurate and affordable field deployment. We aim to address the critical need for sustainable inland water management tools, making this system a valuable contribution to environmental monitoring practices. We also aim to inspire similar development of open-source monitoring systems within our community.

ЕФЕКТИВНІСТЬ ОСВІТНІХ ПЛАТФОРМ ДЛЯ ВИВЧЕННЯ ПРИРОДНИЧИХ ДИСЦИПЛІН

The analysis of the capabilities of modern learning platforms (LPs), their adaptation to the needs of educational institutions of different levels and educational programs is quite important in the modern world. Studying the impact of educational platforms on the quality of knowledge acquired, the activation of creative, cognitive activity and natural science competence in schoolchildren is relevant. The purpose of the study was to establish a statistically significant relationship between the use of educational resources, the quality of education, and the motivation of students to study natural science disciplines. The main objectives are to conduct a qualitative analysis of LPs used in educational institutions; to study the effectiveness of LPs for studying natural sciences; to establish the existence of statistically significant relationships between the use of the MyКlas educational platform and students' motivation to learn in general and to study natural sciences in particular. The article analyzes the level of awareness and motivation of teachers of the Skole Academic Gymnasium (hereinafter - Skole Gymnasium) in the use of various LPs and the possibility of practical implementation during the educational process. It has been proved that there is a statistically significant relationship between the use of educational resources, the quality of education and the motivation of students of Skole Gymnasium to study natural science subjects. The level of students' academic achievement improved after using LP, mainly the MyКlas educational platform. The overall increase in academic performance in the natural sciences (Natural Sciences, Discovering Nature, Biology, Geography, Chemistry, Physics, Biology and Ecology, and the integrated course Natural Sciences) over the past 5 years is 25.7%, and the share of classes that used various tools of educational platforms has increased by 77.8%. The coefficient of determination for the regression analysis was 0.519, which indicates that educational platforms have a statistically significant impact on the quality of education.

Comorbidity problems in patients with osteoporosis

BACKGROUND: Osteoporosis is a metabolic skeletal disease characterized by decreased bone mass, impaired microarchitecture of bone tissue and, as a consequence, fractures with minimal trauma. The social significance of osteoporosis is determined by its consequences — fractures of the vertebral bodies and bones of the peripheral skeleton, leading to high medical costs and causing a high level of disability, including disability and mortality. Osteoporosis is most often combined with cardiovascular diseases caused by atherosclerosis, which are widespread and are the leading cause of disability and mortality. A small number of studies have been devoted to the study of the features of clinical manifestations of cardiovascular diseases in the combination of coronary heart disease and osteoporosis. AIM: To identify the frequency and severity of comorbid diseases and their risk factors in postmenopausal osteoporosis. MATERIALS AND METHODS: A retrospective and prospective cohort study was conducted from 2013 to 2023. 8250 women with postmenopausal osteoporosis were examined and the data from the medical records of these patients who applied to the Osteoporosis Center of the North-Western State Medical University named after I.I. Mechnikov of the Ministry of Health of the Russian Federation from 2013 to 2020 were analyzed. At the prospective stage, two comparison groups of 100 patients with and without postmenopausal osteoporosis were formed from 500 patients with comorbid diseases (coronary heart disease, hypertension, type 2 diabetes, stroke, transient ischemic attack). Bone mineral density was assessed based on the Hologic Discoveri Wi dual-energy X-ray absorptiometry at 2 points in the lumbar spine (L1–L4) and the femoral neck. Along with traditional laboratory examination methods, bone metabolism markers (total calcium, phosphorus, vitamin D, urine analysis for deoxypyridinoline, C-terminal telopeptide of type 1 collagen, osteocalcin, alkaline phosphatase, parathyroid hormone) were assessed dynamically. In all the patients, along with a general clinical examination, an assessment of risk factors for cardiovascular disease and osteoporosis was performed. RESULTS: Body mass index was higher in the group of patients with osteoporosis — 28.4 (26.9–32.4) kg/m2 (p 0.001), versus 27.64 (25.8–30.0) kg/m2 (p 0.05). Bone mineral density in the group of patients with osteoporosis was statistically lower than in the group without osteoporosis. In the group of patients with osteoporosis, the risk of fractures was high and amounted to 37 (95% confidence interval 15.0–38.50) %, p 0.001), versus in the control group 9.55 (95% confidence interval 7.67–15.0) % (p 0.001) according to the FRAX questionnaire for the Russian Federation. In the group of patients with osteoporosis, the Charlson index values were 5.1 (95% confidence interval 4.7–5.6) points. As a result of the correlation analysis, a reliable positive relationship was revealed between low bone mineral density L1–L4 and hip with bone metabolism: alkaline phosphatase, C-terminal telopeptide of type 1 collagen, vitamin D, osteocalcin, urinary deoxypyridinoline. We found a negative relationship between bone mineral density and the Charlson index, absolute ten-year risk of hip fracture and the duration of early menopause, and the level of hypertension. To assess the dependence of QRISK3-2018 on osteoporosis risk factors in the group of patients with osteoporosis, multiple linear regression was performed. This model was characterized by a statistically significant correlation between the factors and the dependent variable of high tightness. The coefficient of determination was 0.608, indicating a 60.8% contribution of the factors taken into account in the model to the variance of QRISK3-2018. CONCLUSIONS: In women with postmenopausal osteoporosis, comorbid pathology is more common, aggravating the course of the disease. Knowledge of the prevalence of concomitant pathology and joint risk factors will make it possible to simultaneously form groups at increased risk of development, which will ensure prevention of both diseases with the same non-medical means and drugs.

DeepJEB: 3D Deep Learning-based Synthetic Jet Engine Bracket Dataset

Abstract Recent advances in artificial intelligence (AI) have impacted various fields, including mechanical engineering. However, the development of diverse, high-quality datasets for structural analysis remains a challenge. Traditional datasets, like the jet engine bracket dataset, are limited by small sample sizes, hindering the creation of robust surrogate models. This study introduces the Deep- JEB dataset, generated through deep generative models and automated simulation pipelines, to address these limitations. DeepJEB offers comprehensive 3D geometries and corresponding structural analysis data. Key experiments validated its effectiveness, showing significant improvements in surrogate model performance. Models trained on DeepJEB achieved up to a 23% increase in the coefficient of determination and over a 70% reduction in mean absolute percentage error (MAPE) compared to those trained on traditional datasets. These results underscore the superior generalization capabilities of DeepJEB. By supporting advanced modeling techniques, such as graph neural networks (GNNs) and convolutional neural networks (CNNs), DeepJEB enables more accurate predictions in structural performance. The DeepJEB dataset is publicly accessible at: https://www.narnia.ai/dataset.

Analysis of the Impact of Financial Technology (Fintech) on the Needs of Generation Z in the Community Around UPI Purwakarta Campus

In the era of technological acceleration, technological advancements affect various aspects of life including the financial sector. Financial Technology (fintech) is an innovation that facilitates financial transactions and online loans, especially for generation Z. However, this convenience is also accompanied by risks such as fraud and consumptive behavior. This study aims to determine the impact and factors that affect Generation Z in financial technology in the community around the UPI Purwakarta campus. The research method used was a quantitative research method with a likert scale and distributed to 435 respondents. The data analysis techniques used are correlation analysis and simple regression analysis. While the hypothesis test uses the determination coefficient and the F test. The results of this study show that there is a significant (simultaneous) influence between the perception of Generation Z and the use of fintech applications and the magnitude of the influence is 31.1%.

Development of a Prediction Model for Specific Fuel Consumption in Rotary Tillage Based on Actual Operation

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor’s transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model’s accuracy was analyzed using the coefficient of determination (R2) and root mean square error (RMSE), and it was compared with the ASABE’s fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R2 of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations.

ANALYSIS OF THE QUALITY OF ACADEMIC ADMINISTRATIVE SERVICES ON STUDENT SATISFACTION OF STIE BENTARA PERSADA

This research examines the quality of services provided by the Sekolah Tinggi Ilmu Ekonomi Bentara Persada (STIE BP) and its influence on the level of student satisfaction with the Academic Administration Bureau services. This research was conducted to determine the influence of reliability, responsiveness, sincerity and tangibility on student satisfaction. This research includes causal-comparative research, namely research to test one variable influencing another variable. Data collection was carried out using a questionnaire distributed to all 73 active STIE BP students. The data analysis technique used is multiple regression analysis, by looking at the t-test and f-test. The results of this study conclude that there is a positive and significant influence of reliability on student satisfaction, there is no positive and significant influence of responsiveness on student satisfaction, there is no positive and significant influence of sincerity (empathy) on student satisfaction, there is a positive and Tangibility has a significant impact on student satisfaction. Simultaneously, the four variables have a positive and significant effect on student satisfaction, with a coefficient of determination (r2) of 0.432 meaning, the four independent variables influence the student satisfaction variable by 43.2%, while the remainder (56.8%) is influenced by other variables not examined in this research.

Waste stabilization pond modelling using extreme gradient boosting machines

ABSTRACT The integrated solar and hydraulic jump-enhanced waste stabilization pond (ISHJEWSP) has been proposed as a solution to enhance performance of the conventional WSP. Despite the better performance of the ISHJEWSP, there is seemingly no previous study that has deployed machine learning (ML) methods in modelling the ISHJEWSP. This study is aimed at determining the relationships between the ISHJEWSP effluent parameters as well as comparing the performance of extra trees (ET), random forest (RF), decision tree (DT), light gradient boosting machine (LightGBM), gradient boosting (GB), and extreme gradient boosting (XGBoost) methods in predicting the effluent biochemical oxygen demand (BOD5) in the ISHJEWSP. The feature importance technique indicated that the most important were pH, temperature, solar radiation, dissolved oxygen (DO), and total suspended solids. These selected features yielded strong correlations with the dependent variable except DO, which had a moderate correlation. With respect to coefficient of determination and root mean square error (RMSE), the XGBoost performed better than the other models [coefficient of determination (R2) = 0.807, mean absolute error (MAE) = 4.3453, RMSE = 6.2934, root mean squared logarithmic error (RMSLE) = 0.1096]. Gradient boosting, XGBoost, and RF correspondingly yielded the least MAE, RMSE, and RMSLE of 3.9044, 6.2934, and 0.1059, respectively. The study demonstrates effectiveness of ML in predicting the effluent BOD5 in the ISHJEWSP.

Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen

Optimizing CO2 injection for enhanced oil recovery (EOR) requires a precise estimation of the CO2-diffusivity coefficient in porous media. This study developed a predictive model for the molecular diffusivity coefficient of CO2 in bitumen and heavy crude oils using a Bayesian regularized artificial neural network. The unique contributions of the developed model compared to existing models include: First, a simple and accurate mathematical correlation between inputs and CO2-diffusivity has been presented, making it easy to use particularly for individuals with limited understanding of machine learning. Secondly, the time it takes the model to make a prediction (its inference latency) has been established and the model has been physically validated using trend analysis. Fourthly, independent datasets were used to test for the model generalizability.The model was evaluated using 260 data points from the literature, with 70 % for training and 30 % for testing. Key performance metrics were calculated, including root mean square error (RMSE = 0.03), and coefficient of determination (R2 = 0.996). Furthermore, an outlier detection analysis using the statistical leverage approach demonstrated that the data used was of high quality. A relevancy factor analysis revealed that pressure had the greatest impact on CO2 diffusivity, followed by temperature, while CO2 mass fraction had the least impact. The developed model operates within a temperature range of 295 K – 363 K and a pressure range of 1 MPa – 8MPa. Overall, the outcomes of this study contribute to the efficient prediction of CO2 diffusivity in heavy crude oil and bitumen.

Sonophotocatalytic degradation of rhodamine B dye using Zr doped ZnO nanoparticles: Kinetic study and toxicity assessment

This study investigates sonophotocatalysis for the oxidation of Rhodamine B (RhB) dye using Zr doped ZnO nanoparticles. The synthesized nanoparticles were characterized by scanning electron microscope, Fourier-transform infrared, X-ray diffraction, Brunauer Emmett-Teller, thermogravimetric analysis, and photoluminescence. The hybrid sonophotocatalysis system achieved 98.73 ± 1.25% degradation of RhB under the conditions: RhB dye concentration = 10 ppm, 4 wt% of Zr doped ZnO = 0.1 g, reaction temperature = 30 °C, time = 60 min, pH = 7. A kinetic model was developed to predict the efficiency of RhB degradation through intrinsic elementary chemical reactions. The high coefficient of determination (R2 = 0.96) indicates the model's prediction accuracy in describing the degradation kinetics of RhB within sonophotocatalysis system. The electrical energy per order (EEO) analysis demonstrated that US + UVC + 4 wt% of Zr doped ZnO significantly reduced EEO (1055 kWh m−3 order−1). The synergy index for this combination was approximately 1.60, demonstrating a significant synergistic effect. Density Functional Theory (DFT) studies were utilized to propose the most probable degradation pathway, identifying reactive sites and potential byproducts. The simulations revealed the central carbon atom (1C site) as highly susceptible to radical attacks, indicating potential decolorization pathways such as N-de-ethylation, chromophore cleavage, and ring opening. Toxicity assessments of both the parent dye and its intermediates were conducted using ECOSAR (Ecological Structure Activity Relationship) to evaluate their ecological impacts. The combination of experimental results and kinetic modeling and simulations offers a deep understanding of RhB degradation complexities, driving advancements in sustainable water treatment technologies.

Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting.

A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study.

Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake. This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients. The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method. The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R2 value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97). The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.

Pengaruh Promosi Dan Harga Terhadap Keputusan Pembelian Honda Beat Pada PT Tunas Honda Natar

This research aims to determine the influence of promotions and prices on purchasing decisions for Honda Beat at PT Tunas Honda Natar. The data used in this research is primary data, namely data collected by the researcher himself directly from the first source or place where the research object was carried out, namely the results of interviews and questionnaires distributed to consumers at PT Tunas Honda Natar. As well as secondary data, namely data collected from other parties, namely books, articles and journals related to this research. The analysis techniques used are validity and reliability tests, classical assumption tests, hypothesis tests, coefficient of determination (R2), and multiple linear regression tests. Based on the results of the analysis, it can be concluded that partially Promotion and Price have a significant effect on Purchasing Decisions and simultaneously Promotion and Price have a significant effect on Purchasing Decisions.